Design of a reliable supply chain network with responsiveness considerations under uncertainty: case study of an Iranian tire manufacturer

Document Type: conference paper

Authors

Department of industrial engineering, Faculty of engineering, Kharazmi university, Tehran, Iran

Abstract

This paper proposes a bi-objective reliable supply chain network design that immunizes the network against different sources of uncertainties. In this regard, scenario based stochastic programming method is applied to model different disruption scenarios affecting accurate performance of network stages. Also, reliable and unreliable facilities are suggested for lessening vulnerability of network against disruptions. To maximize responsiveness of the network, maximal covering concept is applied aside with a new facility reliability measuring method. To achieve to the noted aims, total expected costs of network design is minimized as well as maximizing responsiveness of facilities. Also, a possibilistic flexible programming method is suggested to cope with uncertainty of parameters and flexibility of constraints. The proposed method is capable of controlling risk-aversion of output decisions based on opinion company decision makers. Finally, the model is solved based on the derived from real case study of tire manufacturing and output results are analysed that show applicability and effectiveness of the extended network design model.       

Keywords

Main Subjects


Badri, H., Ghomi, S. F., & Hejazi, T. H. (2017). A two-stage stochastic programming approach for value-based closed-loop supply chain network design. Transportation Research Part E: Logistics and Transportation Review105, 1-17.

 

Behzadi, G., O'Sullivan, M. J., Olsen, T. L., Scrimgeour, F., & Zhang, A. (2017). Robust and resilient strategies for managing supply disruptions in an agribusiness supply chain. International Journal of Production Economics191, 207-220.

 

Cadenas, J. M., & Verdegay, J. L. (1997). Using fuzzy numbers in linear programming. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)27(6), 1016-1022.

 

Cui, J., Zhao, M., Li, X., Parsafard, M., & An, S. (2016). Reliable design of an integrated supply chain with expedited shipments under disruption risks. Transportation Research Part E: Logistics and Transportation Review95, 143-163.

 

Fattahi, M., Govindan, K., & Keyvanshokooh, E. (2017). Responsive and resilient supply chain network design under operational and disruption risks with delivery lead-time sensitive customers. Transportation Research Part E: Logistics and Transportation Review101, 176-200.

 

Fazli-Khalaf, M., Mirzazadeh, A., & Pishvaee, M. S. (2017). A robust fuzzy stochastic programming model for the design of a reliable green closed-loop supply chain network. Human and Ecological Risk Assessment: An International Journal23(8), 2119-2149.

 

Hamidieh, A., & Fazli-Khalaf, M. (2017). A Possibilistic Reliable and Responsive Closed Loop Supply Chain Network Design Model under Uncertainty. Journal of Advanced Manufacturing Systems16(04), 317-338.

 

Hatefi, S. M., & Jolai, F. (2015). Reliable forward-reverse logistics network design under partial and complete facility disruptions. International Journal of Logistics Systems and Management20(3), 370-394.

 

Inuiguchi, M., & Ramık, J. (2000). Possibilistic linear programming: a brief review of fuzzy mathematical programming and a comparison with stochastic programming in portfolio selection problem. Fuzzy sets and systems111(1), 3-28.

 

Lima, C., Relvas, S., & Barbosa-Povoa, A. (2018). Stochastic programming approach for the optimal tactical planning of the downstream oil supply chain. Computers & Chemical Engineering108, 314-336.

 

Liu, B., & Iwamura, K. (1998). Chance constrained programming with fuzzy parameters. Fuzzy sets and systems94(2), 227-237.

 

Mohammaddust, F., Rezapour, S., Farahani, R. Z., Mofidfar, M., & Hill, A. (2017). Developing lean and responsive supply chains: A robust model for alternative risk mitigation strategies in supply chain designs. International Journal of Production Economics183, 632-653.

 

Mousazadeh, M., Torabi, S. A., & Zahiri, B. (2015). A robust possibilistic programming approach for pharmaceutical supply chain network design. Computers & Chemical Engineering82, 115-128.

 

Pasandideh, S. H. R., Niaki, S. T. A., & Asadi, K. (2015). Optimizing a bi-objective multi-product multi-period three echelon supply chain network with warehouse reliability. Expert Systems with Applications42(5), 2615-2623.

 

Peidro, D., Mula, J., Poler, R., & Verdegay, J. L. (2009). Fuzzy optimization for supply chain planning under supply, demand and process uncertainties. Fuzzy sets and systems160(18), 2640-2657.

 

Poudel, S. R., Marufuzzaman, M., & Bian, L. (2016). Designing a reliable bio-fuel supply chain network considering link failure probabilities. Computers & Industrial Engineering91, 85-99.

 

Pishvaee, M. S., & Khalaf, M. F. (2016). Novel robust fuzzy mathematical programming methods. Applied Mathematical Modelling40(1), 407-418.

 

Pishvaee, M. S., Razmi, J., & Torabi, S. A. (2012). Robust possibilistic programming for socially responsible supply chain network design: A new approach. Fuzzy sets and systems206, 1-20.

 

Pishvaee, M. S., & Torabi, S. A. (2010). A possibilistic programming approach for closed-loop supply chain network design under uncertainty. Fuzzy sets and systems161(20), 2668-2683.

 

Quddus, M. A., Chowdhury, S., Marufuzzaman, M., Yu, F., & Bian, L. (2018). A two-stage chance-constrained stochastic programming model for a bio-fuel supply chain network. International Journal of Production Economics195, 27-44.

 

Rezapour, S., Farahani, R. Z., & Pourakbar, M. (2017). Resilient supply chain network design under competition: a case study. European Journal of Operational Research259(3), 1017-1035.

 

Rahmani, D., & Mahoodian, V. (2017). Strategic and operational supply chain network design to reduce carbon emission considering reliability and robustness. Journal of Cleaner Production149, 607-620.

 

Yager, R. R. (1981). A procedure for ordering fuzzy subsets of the unit interval. Information sciences24(2), 143-161.

 

Zarandi, M. H. F., Davari, S., & Sisakht, S. A. H. (2013). The large-scale dynamic maximal covering location problem. Mathematical and Computer Modelling57(3-4), 710-719.